Chapter 10: Information Integration and Synthesis

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Chapter 10 Information Integration and Synthesis
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Information integration

• Many integration tasks,
• Integrating Web query interfaces (search forms)
• Integrating ontologies (taxonomy)
• Integrating extracted data
• Integrating textual information
• We only introduce integration of query
  interfaces.
• Many web sites provide forms to query deep web
• Applications meta-search and meta-query

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Global Query Interface
united.com
airtravel.com
delta.com
hotwire.com
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Constructing global query interface (QI)

• A unified query interface
• Conciseness - Combine semantically
• similar fields over source interfaces
• Completeness - Retain source-specific fields
• User-friendliness Highly related fields
• are close together
• Two-phrased integration
• Interface Matching Identify semantically
  similar fields
• Interface Integration Merge the source query
  interfaces

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Schema matching as correlation mining (He and
Chang, KDD-04)

• Across many sources
• Synonym attributes are negatively correlated
• synonym attributes are semantically alternatives.
• thus, rarely co-occur in query interfaces
• Grouping attributes with positive correlation
• grouping attributes semantically complement
• thus, often co-occur in query interfaces
• A data mining problem (frequent itemset mining)

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1. Positive correlation mining as potential groups
Mining positive correlations
Last Name, First Name
2. Negative correlation mining as potential
matchings
Author Last Name, First Name
Mining negative correlations
3. Matching selection as model construction
Author (any) Last Name, First Name
Subject Category
Format Binding
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A clustering approach to schema matching (Wu et
al. SIGMOD-04)

• Hierarchical modeling
• Bridging effect
• a2 and c2 might not look similar themselves
  but they might both be similar to b3
• 1m mappings
• Aggregate and is-a types
• User interaction helps in
• learning of matching thresholds
• resolution of uncertain mappings

X
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Hierarchical Modeling
Ordered Tree Representation
Source Query Interface
Capture ordering and grouping of fields
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Find 11 Mappings via Clustering
Initial similarity matrix
Interfaces
After one merge

• Similarity functions
• linguistic similarity
• domain similarity

, final clusters
a1,b1,c1, b2,c2,a2,b3
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Bridging Effect
A
?
B
C
Observations - It is difficult to match
vehicle field, A, with make field, B - But
As instances are similar to Cs, and Cs label
is similar to Bs - Thus, C might serve as a
bridge to connect A and B!

Note Connections might also be made via labels
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Complex Mappings
Aggregate type contents of fields on the many
side are part of the content of field on the one
side
Commonalities (1) field proximity, (2) parent
label similarity, and (3) value characteristics
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Complex Mappings (Contd)
Is-a type contents of fields on the many side
are sum/union of the content of field on the one
side
Commonalities (1) field proximity, (2) parent
label similarity, and (3) value characteristics
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Instance-based matching via query probing (Wang
et al. VLDB-04)

• Both query interfaces and returned results
  (called instances) are considered in matching.
• Assume a global schema (GS) is given and a set of
  instances are also given.
• The method uses each instance value (IV) of every
  attribute in GS to probe the underlying database
  to obtain the count of IV appeared in the
  returned results.
• These counts are used to help matching.
• It performs matches of
• Interface schema and global schema,
• result schema and global schema, and
• interface schema and results schema.

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Query interface and result page
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Knowledge Synthesis

• Web search paradigm
• Given a query, a few words
• A search engine returns a ranked list of pages.
• The user then browses and reads the top-ranked
  pages to find what s/he wants.
• Sufficient for navigational queries
• if one is looking for a specific piece of
  information, e.g., homepage of a person, a paper.
• Not sufficient for informational queries
• open-ended research or exploration, for which
  more can be done.

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Knowledge/Information Synthesis

• A growing trend among web search engines
• Go beyond the traditional paradigm of presenting
  a list of pages ranked by relevance
• to provide more varied, comprehensive information
  about the search topic.
• Example Categories, related searches
• Going beyond Can a system provide the complete
  information of a search topic? I.e.,
• Find and combine related bits and pieces
• to provide a coherent picture of the topic.

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Bing search of cell phone
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Knowledge synthesis a case study

• Motivation traditionally, when one wants to
  learn about a topic,
• one reads a book or a survey paper.
• With the rapid expansion of the Web, this habit
  is changing.
• Learning in-depth knowledge of a topic from the
  Web is becoming increasingly popular.
• Webs convenience
• Richness of information, diversity, and
  applications
• For emerging topics, it may be essential - no
  book.
• Can we mine a book from the Web on a topic?
• Knowledge in a book is well organized the
  authors have painstakingly synthesize and
  organize the knowledge about the topic and
  present it in a coherent manner.

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An example

• Given the topic data mining, can the system
  produce the following, a concept hierarchy?
• Classification
• Decision trees
• (Web pages containing the descriptions of the
  topic)
• Naïve bayes
• Clustering
• Hierarchical
• Partitioning
• K-means
• .
• Association rules
• Sequential patterns

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Exploiting information redundancy

• Web information redundancy many Web pages
  contain similar information.
• Observation 1 If some phrases are mentioned in a
  number of pages, they are likely to be important
  concepts or sub-topics of the given topic.
• This means that we can use data mining to find
  concepts and sub-topics
• What are candidate words or phrases that may
  represent concepts of sub-topics?

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Each Web page is already organized

• Observation 2 The contents of most Web pages are
  already organized.
• Different levels of headings
• Emphasized words and phrases
• They are indicated by various HTML emphasizing
  tags, e.g., ltH1gt, ltH2gt, ltH3gt, ltBgt, ltIgt, etc.
• We utilize existing page organizations to find a
  global organization of the topic.
• Cannot rely on only one page because it is often
  incomplete, and mainly focus on what the page
  authors are familiar with or are working on.

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Using language patterns to find sub-topics

• Certain syntactic language patterns express some
  relationship of concepts.
• The following patterns represent hierarchical
  relationships, concepts and sub-concepts
• Such as
• For example (e.g.,)
• Including
• E.g., There are many clustering techniques
  (e.g., hierarchical, partitioning, k-means,
  k-medoids).

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Put them together

• Crawl the set of pages (a set of given documents)
• Identify important phrases using
• HTML emphasizing tags, e.g., lth1gt,,lth4gt, ltbgt,
  ltstronggt, ltbiggt, ltigt, ltemgt, ltugt, ltligt, ltdtgt.
• Language patterns.
• Perform data mining (frequent itemset mining) to
  find frequent itemsets (candidate concepts)
• Data mining can weed out peculiarities of
  individual pages to find the essentials.
• Eliminate unlikely itemsets (using heuristic
  rules).
• Rank the remaining itemsets, which are main
  concepts.

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Additional techniques

• Segment a page into different sections.
• Find sub-topics/concepts only in the appropriate
  sections.
• Mutual reinforcements
• Using sub-concepts search to help each other
• Finding definition of each concept using
  syntactic patterns (again)
• is are adverb called known as defined
  as concept
• concept refer(s) to satisfy(ies)
• concept is are determiner
• concept is are adverb being used to
  used to referred to employed to defined as
  formalized as described as concerned with
  called

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Some concepts extraction results

• Data Mining
• Clustering
• Classification
• Data Warehouses
• Databases
• Knowledge Discovery
• Web Mining
• Information Discovery
• Association Rules
• Machine Learning
• Sequential Patterns
• Web Mining
• Web Usage Mining
• Web Content Mining
• Data Mining
• Webminers
• Text Mining
• Personalization
• Information Extraction

Clustering Hierarchical K means Density
based Partitioning K medoids Distance based
methods Mixture models Graphical
techniques Intelligent miner Agglomerative Graph
based algorithms
Classification Neural networks Trees Naive
bayes Decision trees K nearest neighbor Regression
Neural net Sliq algorithm Parallel
algorithms Classification rule learning ID3
algorithm C4.5 algorithm Probabilistic models
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Finding concepts and sub-concepts

• As we discussed earlier, syntactic language
  patterns do convey some semantic relationships.
• Earlier work by Hearst (Hearst, SIGIR-92) used
  patterns to find concepts/sub-concepts relations.
  
• WWW-04 has two papers on this issue (Cimiano,
  Handschuh and Staab 2004) and (Etzioni et al
  2004).
• apply lexicon-syntactic patterns such as those
  discussed 5 slides ago and more
• Use a search engine to find concepts and
  sub-concepts (class/instance) relationships.

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PANKOW (Cimiano, Handschuh and Staab WWW-04)

• The linguistic patterns used are (the first 4 are
  from (Hearst SIGIR-92))
• 1 ltconceptgts such as ltinstancegt
• 2 such ltconceptsgts as ltinstancegt
• 3 ltconceptsgts, (especiallyincluding)ltinstancegt
• 4 ltinstancegt (andor) other ltconceptgts
• 5 the ltinstancegt ltconceptgt
• 6 the ltconceptgt ltinstancegt
• 7 ltinstancegt, a ltconceptgt
• 8 ltinstancegt is a ltconceptgt

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Steps

• PANKOW categorizes instances into given concept
  classes, e.g., is Japan a country or a
  hotel?
• Given a proper noun (instance), it is introduced
  together with given ontology concepts into the
  linguistic patterns to form hypothesis phrases,
  e.g.,
• Proper noun Japan
• Given concepts country, hotel.
• Japan is a country, Japan is a hotel .
• All the hypothesis phrases are sent to Google.
• Counts from Google are collected

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Categorization step

• The system sums up the counts for each instance
  and concept pair (iinstance, cconcept,
  ppattern).
• The candidate proper noun (instance) is given to
  the highest ranked concept(s)
• I instances, C concepts

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KnowItAll (Etzioni et al WWW-04 and AAAI-04)

• Basically use the same approach of linguistic
  patterns and Web search to find
  concept/sub-concept (also called class/instance)
  relationships.
• KnowItAll has more sophisticated mechanisms to
  assess the probability of every extraction, using
  Naïve Bayesian classifiers.
• It thus does better in class/instance extraction.

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Syntactic patterns used in KnowItAll

• NP1 , such as NPList2
• NP1 , and other NP2
• NP1 , including NPList2
• NP1 , is a NP2
• NP1 , is the NP2 of NP3
• the NP1 of NP2 is NP3

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Main Modules of KnowItAll

• Extractor generate a set of extraction rules for
  each class and relation from the language
  patterns. E.g.,
• NP1 such as NPList2 indicates that each NP in
  NPList2 is an instance of class NP1. He visited
  cities such as Tokyo, Paris, and Chicago.
• KnowItAll will extract three instances of class
  CITY.
• Search engine interface a search query is
  automatically formed for each extraction rule.
  E.g., cities such as. KnowItAll will
• Search with a number of search engines
• Download the returned pages
• Apply extraction rule to appropriate sentences.
• Assessor Each extracted candidate is assessed to
  check its likelihood for being correct. Here it
  uses Point-Mutual Information and a Bayesian
  classifier.

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Summary

• Information Integration and Knowledge synthesis
  are becoming important as we move up the
  information food chain.
• The questions is Can a system provide a coherent
  and complete picture about a topic rather than
  only bits and pieces from multiple sites?
• Key Exploiting information redundancy on the
  Web, and NLP.
• More research is needed.